Weighted Krylov-Levenberg-Marquardt method for canonical polyadic tensor decomposition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F20%3A00523836" target="_blank" >RIV/67985556:_____/20:00523836 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICASSP40776.2020.9054312" target="_blank" >http://dx.doi.org/10.1109/ICASSP40776.2020.9054312</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICASSP40776.2020.9054312" target="_blank" >10.1109/ICASSP40776.2020.9054312</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Weighted Krylov-Levenberg-Marquardt method for canonical polyadic tensor decomposition

Popis výsledku v původním jazyce

Weighted canonical polyadic (CP) tensor decomposition appears in a wide range of applications. A typical situation where the weighted decomposition is needed is when some tensor elements are unknown, and the task is to fill in the missing elements under the assumption that the tensor admits a low-rank model. The traditional methods for large-scale decomposition tasks are based on alternating least-squares methods or gradient methods. Second-order methods might have significantly better convergence, but so far they were used only on small tensors. The proposed Krylov-Levenberg-Marquardt method enables to do second-order-based iterations even in large-scale decomposition problems, with or without weights. We show in simulations that the proposed technique can outperform existing state-of-the-art algorithms in some scenarios.

Název v anglickém jazyce

Weighted Krylov-Levenberg-Marquardt method for canonical polyadic tensor decomposition

Popis výsledku anglicky

Weighted canonical polyadic (CP) tensor decomposition appears in a wide range of applications. A typical situation where the weighted decomposition is needed is when some tensor elements are unknown, and the task is to fill in the missing elements under the assumption that the tensor admits a low-rank model. The traditional methods for large-scale decomposition tasks are based on alternating least-squares methods or gradient methods. Second-order methods might have significantly better convergence, but so far they were used only on small tensors. The proposed Krylov-Levenberg-Marquardt method enables to do second-order-based iterations even in large-scale decomposition problems, with or without weights. We show in simulations that the proposed technique can outperform existing state-of-the-art algorithms in some scenarios.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA17-00902S" target="_blank" >GA17-00902S: Pokročilé metody slepé separace podprostorů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2020 IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP 2020

ISBN

978-1-5090-6631-5

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

3917-3921

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Barcelona

Datum konání akce

4. 5. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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